Rock Physics inversion for fluid saturation and porosity prediction

2022 ◽  
Author(s):  
A. Shubin ◽  
D. Klyazhnikov ◽  
V. Ryzhkov
Keyword(s):  
Rock Physics ◽  
Fluid Saturation

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>

scholarly journals Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study

2021 ◽  
Vol 13 (1) ◽  
pp. 1476-1493
Author(s):  
Urooj Shakir ◽  
Aamir Ali ◽  
Muhammad Raiees Amjad ◽  
Muyyassar Hussain
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Integrated Approach ◽  
Poor Quality ◽  
Indus Basin ◽  
Petrophysical Properties ◽  
Gas Field ◽  
Facies Classification ◽  
Fluid Saturation ◽  
Reservoir Facies

Abstract Rock physics provides a dynamic tool for quantitative analysis by developing the basic relationship between fluid, lithological, and depositional environment of the reservoir. The elastic attributes such as impedance, density, velocity, V p/V s ratio, Mu-rho, and Lambda-rho are crucial parameters to characterize reservoir and non-reservoir facies. Rock physics modelling assists like a bridge to link the elastic properties to petrophysical properties such as porosity, facies distribution, fluid saturation, and clay/shale volume. A robust petro-elastic relationship obtained from rock physics models leads to more precise discrimination of pay and non-pay facies in the sand intervals of the study area. The Paleocene aged Lower Ranikot Formation and Pab sandstone of Cretaceous age are proven reservoirs of the Mehar gas field, Lower Indus Basin. These sands are widely distributed in the southwestern part of the basin and are enormously heterogeneous, which makes it difficult to distinguish facies and fluid content in the reservoir intervals. So, an attempt is made in this paper to separate the reservoir facies from non-reservoir facies by using an integrated approach of the petro-elastic domain in the targeted sand intervals. Furthermore, missing logs (S-sonic and P-sonic) were also synthesized in the wells and missing intervals along with improving the poor quality of the density log by captivating the washouts and other side effects. The calibrated rock physics model shows good consistency between measured and modelled logs. Petro-elastic models were predicted initially using petrophysical properties and incorporated at true reservoir conditions/parameters. Lithofacies were defined based on petrophysical cut-offs. Rock physics modelled elastic properties (Lambda-rho versus Mu-rho, impedance versus V p/V s ratio) were then cross-plotted by keeping lithofacies in the Z-axis. The cross-plots clearly separated and demarcated the litho-fluid classes (wet sand, gas sand, shale, and limestone) with specific orientation/patterns which were randomized in conventional petrophysical analysis.

Time-lapse seismic monitoring methodologies applied to the Pyrenees Field, offshore Western Australia

2015 ◽  
Vol 55 (2) ◽  
pp. 412 ◽  
Author(s):  
Ramses Meza ◽  
Guy Duncan ◽  
Konstantinos Kostas ◽  
Stanislav Kuzmin ◽  
Mauricio Florez ◽  
...  
Keyword(s):  
Seismic Response ◽  
Coming Out ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Gas Field ◽  
Field Development ◽  
Fluid Saturation ◽  
Band Limited ◽  
4D Seismic

Time-lapse dedicated 3D seismic surveys were acquired across the Pyrenees oil and gas field, Exmouth Sub-basin to map production-induced changes in the reservoir. Rock-physics 4D modelling showed that changes in pore pressure and fluid saturation would produce a time-lapse seismic response of sufficient magnitude, in both amplitude and velocity, to overcome time-lapse noise. The dominant observed effect is associated with gas coming out of solution. The reservoir simulation model forecasted that reservoir depletion would cause gas breakout that would impact the elastic properties of the reservoir. The effect of gas breakout can be clearly observed on the 4D seismic data as a change in both amplitude and velocity. The analysis of the seismic datasets was proven to be enhanced significantly by using inversion methodologies. These included a band-limited extended-elastic impedance (EEI) approach, as well as simultaneous 4D elastic inversion. These datasets, combined with rock physics modelling, enabled quantitative interpretation of the change in 4D seismic response which was a key tool for assisting with the infill well placement and field development strategy.

Seismic estimation of fluid saturation based on rock physics: A case study of the tight-gas sandstone reservoirs in Ordos Basin

2021 ◽  
pp. 1-47
Author(s):  
Chao Li ◽  
Peng Hu ◽  
Jing Ba ◽  
José M. Carcione ◽  
Tianwen Hu ◽  
...  
Keyword(s):  
Ordos Basin ◽  
Rock Physics ◽  
Gas Production ◽  
P Wave ◽  
Quantitative Prediction ◽  
Rock Fragment ◽  
Tight Gas ◽  
Fluid Saturation ◽  
Sandstone Reservoirs ◽  
Tight Gas Sandstone

Tight-gas sandstone reservoirs of the Ordos Basin of China are characterized by high rock-fragment content, dissimilar pore types and a random distribution of fluids, leading to strong local heterogeneity. We model the seismic properties of these sandstones with the double-double porosity (DDP) theory, which considers water saturation, porosity and the frame characteristics. A generalized seismic wavelet is used to fit the real wavelet and the peak frequency-shift method is combined with the generalized S-transform to estimate attenuation. Then, we establish rock-physics templates (RPTs) based on P-wave attenuation and impedance. We use the log data and related seismic traces to calibrate the RPTs and generate a 3D volume of rock-physics attributes for the quantitative prediction of saturation and porosity. The predicted values are in good agreement with the actual gas production reports, indicating that the method can be effectively applied to heterogeneous tight-gas sandstone reservoirs.

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>

Seismic screening of rock stiffness and fluid softening using rock-physics attributes

2015 ◽  
Vol 3 (4) ◽  
pp. SAE85-SAE93 ◽  
Author(s):  
Per Avseth ◽  
Tor Veggeland
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Well Log ◽  
Reservoir Properties ◽  
Empirical Relationships ◽  
Norwegian Sea ◽  
Reservoir Rocks ◽  
Fluid Saturation ◽  
Reservoir Sandstones ◽  
Elastic Impedance

We have developed a methodology to create easy-to-implement rock-physics attributes that can be used to screen for reservoir sandstones and hydrocarbon pore fill from seismic inversion data. Most seismic attributes are based on the empirical relationships between reservoir properties and seismic observables. We have honored the physical properties of the rocks by defining attributes that complied with calibrated rock-physics models. These attributes included the fluid saturation sensitive curved pseudo-elastic impedance (CPEI) and the rock stiffness/lithology attribute pseudo-elastic impedance for lithology (PEIL). We found that the CPEI attribute correlated nicely with saturation and resistivity, whereas the PEIL attribute in practice was a scaled version of the shear modulus and correlated nicely with porosity. We determined the use of these attributes on well log and seismic inversion data from the Norwegian Sea, and we successfully screened out reservoir rocks filled with either water or hydrocarbons.

Fluid-pressure discrimination in anisotropic reservoir rocks — A sensitivity study

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. O1-O11 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Fluid Pressure ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Sand Layer ◽  
Weak Anisotropy ◽  
Fluid Saturation

We investigate how seismic anisotropy influences our ability to distinguish between various production-related effects from time-lapse seismic data. Based on rock physics models and ultrasonic core measurements, we estimate variations in PP and PS reflectivity at the top reservoir interface for fluid saturation and pore pressure changes. The tested scenarios include isotropic shale, weak anisotropic shale, and highly anisotropic shale layers overlaying either an isotropic reservoir sand layer or a weak anisotropic sand layer. We find that, for transverse isotropic media with a vertical symmetry axis (TIV), the effect of weak anisotropy in the cap rock does not lead to significant errors in, for instance, the simultaneous determination of pore-pressure and fluid-saturation changes. On the other hand, changes in seismic anisotropy within the reservoir rock (caused by, for instance, increased fracturing) might be detectable from time-lapse seismic data. A new method using exact expressions for PP and PS reflectivity, including TIV anisotropy, is used to determine pressure and saturation changes over production time. This method is assumed to be more accurate than previous methods.

scholarly journals Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. M35-M53 ◽  
Author(s):  
Bastien Dupuy ◽  
Stéphane Garambois ◽  
Jean Virieux
Keyword(s):  
Input Data ◽  
Reservoir Characterization ◽  
Quantitative Estimation ◽  
A Priori ◽  
Rock Physics ◽  
Fluid Phase ◽  
Quality Factors ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Wide Range

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

Porosity inference and classification of siliciclastic rocks from multiple data sets

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. O65-O76 ◽  
Author(s):  
Luiz G. Loures ◽  
Fernando S. Moraes
Keyword(s):  
Gamma Ray ◽  
Synthetic Data ◽  
Rock Physics ◽  
Real Data ◽  
Calibration Procedure ◽  
Data Sets ◽  
Joint Inference ◽  
Fluid Saturation ◽  
Multiple Data ◽  
Multiple Data Sets

We develop a Bayesian formulation for joint inference of porosity and clay volume, incorporating multiple data sets, prior information, and rock physics models. The derivation is carried out considering the full uncertainty involved in calculations from unknown hyperparameters required by either rock physics equations (model coefficients) or statistical models (data variances). Eventually, data variances are marginalized in closed form, and the model coefficients are fixed using a calibration procedure. To avoid working with a high-dimension probability density function in the parameter space, our formulation is derived and implemented using a moving window along the data domain. In thisway, we compute a collection of 2D posterior distributions forinterval porosity and clay volume, corresponding to each positionalong the window’s path. We test the methodology on both synthetic and real well logs consisting of gamma-ray, neutron, compressional and shear sonic velocity, and density. Tests demonstrate that integrating the relevant pieces of information about porosity and clay volume reduces the uncertainties associated with the estimates. Error analysis of a synthetic data example shows that neutron and density logs provide more information about porosity, whereas gamma-ray logs and velocities provide more information about clay volume. Additionally, we investigate a change in fluid saturation as a source of systematic error in porosity prediction. A real data example, incorporating porosity measurements on core samples, further demonstrates the consistency of our methodology in reducing the uncertainties associated with our final estimates.

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